- The first page in our sample app … is a simple conversational UI … that allows the user to type in what they want to do … and the system to react to it. … Based on how we set up our natural language classifier, … the system can react to user intents … to give a greeting, ask for help, do visual recognition, … predict house votes based on our custom model, … and even deal with things that we don't understand. … To tie in the natural language classifier, … we will open the main view model in the view models folder. … And we want to look for a particular method … called get intent for text. … So we'll type that in. … Get intent from text. … And here it is. … We can see that anything the classifier returns … that is less than 55% certain … will be the same as if the system … didn't respond to the user. … So this is a way that we as the programmer can say … "Hey, if our machine algorithm … "isn't really certain of something, … "we're going to say that it doesn't know." … Right, so anything that our algorithm returns back …
- Defining machine learning
- Training a machine learning model
- Comparing machine learning frameworks
- Using IBM Watson for mobile machine learning
- Using Azure Machine Learning for speech and image recognition
- Training Core ML models
- Comparing client-side and server-side models
Skill Level Beginner
Xamarin and Android Studio: Material Designwith Kevin Ford1h 47m Intermediate
Machine Learning for iOS Developerswith Brian Advent1h 25m Advanced
1. Introduction to Machine Learning
2. Server Models: IBM Watson
3. Server Models: Azure Machine Learning
4. Client Models: Core ML
5. Understanding the Offerings
Next steps1m 40s
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